1. Technological Field
The present disclosure relates generally to artificial neural networks, and more particularly in one exemplary aspect to computer apparatus and methods for plasticity implementation in a pulse-code neural network.
2. Description of Related Art
Artificial spiking neural networks are frequently used to gain an understanding of biological neural networks, and for solving artificial intelligence problems. These networks typically employ a pulse-coded mechanism, which encodes information using timing of the pulses. Such pulses (also referred to as “spikes” or ‘impulses’) are short-lasting (typically on the order of 1-2 ms) discrete temporal events. Several exemplary embodiments of such encoding are described in commonly owned and co-pending U.S. patent application Ser. No. 13/152,084 entitled “APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION”, filed Jun. 2, 2011, and co-owned U.S. patent application Ser. No. 13/152,119,filed Jun. 2, 2011, entitled “SENSORY INPUT PROCESSING APPARATUS AND METHODS”, and patented as U.S. Pat. No. 8,942,466 on Jan. 27, 2015, each incorporated herein by reference in its entirety.
Typically, artificial spiking neural networks, such as the exemplary network described in owned U.S. patent application Ser. No. 13/541,531, entitled “CONDITIONAL PLASTICITY SPIKING NEURON NETWORK APPARATUS AND METHODS”, may comprise a plurality of units (or nodes), which correspond to neurons in a biological neural network. Any given unit may be connected to many other units via connections, also referred to as communications channels, and/or synaptic connections. The units providing inputs to any given unit are commonly referred to as the pre-synaptic units, while the unit receiving the inputs is referred to as the post-synaptic unit.
Each of the unit-to-unit connections may be assigned, inter alia, a connection efficacy, which in general may refer to a magnitude and/or probability of input spike influence on unit output response (i.e., output spike generation/firing). The efficacy may comprise, for example a parameter—synaptic weight—by which one or more state variables of post-synaptic unit are changed. During operation of a pulse-code network, synaptic weights may be dynamically adjusted using what is referred to as the spike-timing dependent plasticity (STDP) in order to implement, among other things, network learning. In some implementations, larger weights may be associated with a greater effect a synapse has on the activity of the post-synaptic neuron.
In some existing plasticity implementations, connections that deliver inputs (to a given unit) prior to generation of post-synaptic response may be potentiated, while connections that deliver inputs after the generation of the post-synaptic response may be depressed. The choice of plasticity functional dependence may determine network behavior. Accordingly, various implementations plasticity mechanisms exist including, for example, the use of target connection efficiency (that may be defined as a ratio of a number of input (pre-synaptic) spikes Nfire delivered to a neuron via the connection that are followed by neuron response (e.g., post-synaptic spike) generation, to the total number of input spikes Ntot delivered to the neuron via the connection. However, existing plasticity implementations do not always provide for network behavior, particularly when input characteristics change.
Consequently there is a salient need for improved adaptive plasticity mechanisms to enable a spiking neuron network capable of operating in a wide variety of input and network dynamic regimes.